What is Case Management

What is Case Management?

Efficient and profitable business operations demand precise management of various tasks. However, not all processes are straightforward. Case management is an approach used for handling unique cases that are more complex and fall outside of the ordinary tasks automated on a predetermined path. Cases require specific attention and have variables that need to be managed, tracked, and resolved with greater agility. They require increased human intervention, collaboration, and complex decision-making along with automation to be managed effectively, which is what a case management system accomplishes.

What Constitutes a Case?

Processing an invoice with automation typically isn’t considered a case in the traditional sense. Instead, it’s a repetitive, rule-based task that can be standardized and automated efficiently with workflows.

In contrast, insurance claims, loan applications, or customer service issues are cases that can have many permutations, calling for multiple human interactions for decisioning. Cases normally do not progress on a linear path and may involve more communication to resolve them. The course to closing a case generally varies from one case to another.

Depending on the industry there could easily be hundreds of unique cases per week when considering all departments and operational activities that are essential for the functioning of the business. An insurance company could potentially handle even thousands of cases per week, contingent on the size of the company, the volume of policies, and the complexity of claims and inquiries. The same is true for financial, healthcare, and other industries, and as a business grows in size and complexity, the number tends to increase accordingly.

How Case Management Functions

Sticking with an example of an insurance company, consider all of the twists and turns that can occur from case inception to resolution. The diverse nature of an insurance claim and the number of processing steps involved depicts how convoluted claim processing can be.

Trying to manage these processes without technology isn’t a viable or cost-effective option today. Any of the stages in claims processing can alter the path to closing out a claim, requiring quick adaptability. A need for more information, for instance, can initiate what looks like a step backward to gather more details and documentation from a customer before moving the case forward.

Regardless of how a case progresses, multiple stakeholders, systems, and data points may be involved in making decisions to advance a case, exemplifying the agility required for effective and efficient case management. A case management system provides a centralized platform to unify all of the data, documentation, humans, systems, and automated processes to work harmoniously together to achieve the end goal.

Understanding the Benefits of a Case Management System

By implementing case management software, businesses can manage the most complex cases, ensure accuracy, reduce risks, and reach resolution faster. Benefits include:

The Speed and Precision of Intelligent Case Management

The immense capabilities of AI and machine learning super charge case management. The power of AI aids traditional approaches by delving deep into data analysis and making intelligent decisions, delivering unparalleled value across all cases, especially the most dynamic in nature.

 Through AI algorithms and automation, intelligent case management excels in:

• Automated Case Classification: Sort incoming cases according to their content, giving priority and routing them to appropriate teams for resolution.
• Comprehension of Natural Language: Extract pertinent details from unorganized text in cases, assisting in identifying issues and generating insights.
• Automated Replies: Formulates automated responses for frequently asked questions, minimizing human involvement and boosting productivity.
• Ongoing Improvement: Through continuous learning, AI assimilates insights from resolved cases and user input, remaining current and enhancing its efficiency with time.

Variations of AI are in use today, and how you use them depends on what problems you want to solve. Often different AI models are used simultaneously and may include:

• Machine learning (ML): Enables computers to learn from data to improve performance and tasks. 
• Large language models (LLMs): Understands and generates human-like text on a vast scale.
• Natural language processing (NLP): Focuses on enabling computers to understand, interpret, and generate human language.
• Computer vision (CV): Interprets and understands the visual world through digital images or videos.
• Robotic process automation (RPA): Involves using software robots to automate repetitive tasks and streamline business processes without human intervention.

From safeguarding against fraud and managing risks to fostering customer relationships and ensuring regulatory compliance, intelligent case management equips organizations to tackle complex challenges with precision and achieve extraordinary results.

Real-life Use of AI in Case Management

Referring once again to processing insurance claims and using the same stages listed above, how might AI improve case management? 

Case Initiation:

• AI-powered chatbots can streamline claim submission and customer inquiries.
• Natural Language Processing (NLP) algorithms can analyze the incident description to extract relevant information and classify the type of claim.

Data Gathering:

• AI can automate the collection of documentation by scanning and extracting information from documents, photographs, and other sources.
• NLP can parse through witness statements, police reports, and other textual data to identify crucial details.

Initial Assessment:

• AI algorithms can analyze policy documents and historical claim data to assess coverage and determine the initial validity of the claim.
• Machine learning models can identify patterns indicative of fraudulent claims, flagging them for further investigation.

Claim Processing:

• AI-based systems can automate administrative tasks such as assigning claim numbers, updating records, and generating notifications.
• AI can help streamline repetitive tasks, reducing the time and effort required for claim processing.

Investigation:

• AI can assist in identifying relevant parties for interviews, inspections, or consultations based on the nature of the claim and historical data.
• Predictive analytics can prioritize claims for further investigation based on risk factors or anomaly detection.

Evaluation:

• AI models can estimate repair costs, medical expenses, and other losses by analyzing historical data, industry benchmarks, and real-time information.
• Advanced analytics can assess the potential impact of different factors on claim outcomes, aiding in decision-making.

Negotiation:

• AI-powered systems can analyze negotiation strategies based on historical data and market trends, providing recommendations for optimal outcomes.
• Natural Language Generation (NLG) can generate personalized responses or settlement offers tailored to specific claimants.

Documentation and Communication:

• AI can automate the generation of case notes, correspondence, and reports, ensuring consistency and accuracy in communication.
• AI can provide real-time updates to claimants and stakeholders, reducing the need for manual follow-ups.

Legal Proceedings:

• AI can assist by analyzing case law, precedent, and relevant statutes to support legal arguments and decision-making.
• Predictive analytics can assess the likelihood of success in different legal strategies, guiding litigation efforts.

Resolution:

• AI can facilitate the negotiation and settlement process by analyzing historical data and simulating different scenarios to optimize outcomes.
• Automated workflows can expedite the resolution process by coordinating tasks and approvals across departments.

Post-Resolution Activities:

• AI can automate post-resolution tasks such as closing out claims, updating records, and processing payments, reducing manual effort and errors.
• Predictive analytics can identify potential issues or trends in post-resolution activities, enabling proactive intervention.

Process Mining in Case Management

Process mining is another useful tool to incorporate with case management to increase visibility into processes and optimize them further by:

• Detecting patterns, trends, and irregularities within case data
• Identifying bottlenecks and prioritizing cases based on urgency and impact
• Recommending tailored actions and interventions based on unique case characteristics
• Enhancing resolution times, mitigating risks, and facilitating continuous optimization

Process mining is particularly important for case management and the extensive processes involved. This functionality not only enables visibility into existing automated processes but also identifies processes that should be automated for better performance and optimization. Using a process mining tool that provides instant insight analysis with a single click is especially beneficial for a real-time view and constantly identifying issues.

How Decisions Helps

Case management is critical, but it can be especially time and labor intensive, fraught with unpredictability, unforeseen risks, and increased costs. Decisions for case management, can transform how your business manages cases with a platform to handle any case with:

• an unmatched rules engine,
• automated and adaptable workflows,
• AI for intelligent case management,
• a low-code solution,
• and a highly collaborative platform. 

The payoff for your business is incredibly streamlined operations, increased productivity, cost and time savings, reduced errors, greater customer satisfaction, and a distinct competitive edge.

Change the Way You Work

Decisions improves every process, managing every case—from the simple to the complex—with agility, speed, and intelligent decision-making to optimize business operations, mitigate risks, and achieve exceptional outcomes.

Schedule a time to discover all the cases in your organization that you can radically transform with Decisions.